Domain Generalization (DG) involves training the model in different but related data distributions, and evaluating in an unseen domain. For example, a common benchmark consists of domains created by rotating images in the MNIST dataset by different angles. Constrained formulations have been proposed in this context [1]}, [2]}, that enforce invariance under learnt domain translation transformations. In contrast, our approach addresses pre-defined transformations that are commonly used in data augmentation pipelines in order to improve generalization in the same domain used to train the model.
